Field of the Invention
This invention relates generally to the field of motion capture. More particularly, the invention relates to an improved apparatus and method for performing motion capture using a random pattern of paint applied to a portion of a performer's face, body, clothing, and/or props.
Description of the Related Art
“Motion capture” refers generally to the tracking and recording of human and animal motion. Motion capture systems are used for a variety of applications including, for example, video games and computer-generated movies. In a typical motion capture session, the motion of a “performer” is captured and translated to a computer-generated character.
As illustrated in FIG. 1 in a traditional motion capture system, a plurality of motion tracking “markers” (e.g., markers 101, 102) are attached at various points on a performer's 100's body. The points are typically selected based on the known limitations of human anatomy. Different types of motion capture markers are used for different motion capture systems. For example, in a “magnetic” motion capture system, the motion markers attached to the performer are active coils which generate measurable disruptions x, y, z and yaw, pitch, roll in a magnetic field.
By contrast, in an optical motion capture system, such as that illustrated in FIG. 1, the markers 101, 102 are passive spheres comprised of retroreflective material, i.e., a material which reflects light back in the direction from which it came, ideally over a wide range of angles of incidence. A plurality of cameras 120, 121,122, each with a ring of LEDs 130, 131, 132 around its lens, are positioned to capture the LED light reflected back from the retroreflective markers 101, 102 and other markers on the performer. Ideally, the retroreflected LED light is much brighter than any other light source in the room. Typically, a thresholding function is applied by the cameras 120, 121,122 to reject all light below a specified level of brightness which, ideally, isolates the light reflected off of the reflective markers from any other light in the room and the cameras 120, 121, 122 only capture the light from the markers 101, 102 and other markers on the performer.
A motion tracking unit 150 coupled to the cameras is programmed with the relative position of each of the markers 101, 102 and/or the known limitations of the performer's body. Using this information and the visual data provided from the cameras 120-122, the motion tracking unit 150 generates artificial motion data representing the movement of the performer during the motion capture session.
A graphics processing unit 152 renders an animated representation of the performer on a computer display 160 (or similar display device) using the motion data. For example, the graphics processing unit 152 may apply the captured motion of the performer to different animated characters and/or to include the animated characters in different computer-generated scenes. In one implementation, the motion tracking unit 150 and the graphics processing unit 152 are programmable cards coupled to the bus of a computer (e.g., such as the PCI and AGP buses found in many personal computers). One well known company which produces motion capture systems is Motion Analysis Corporation (see, e.g., www.motionanalysis.com).
One problem which exists with current marker-based motion capture systems is that when the markers move out of range of the cameras, the motion tracking unit 150 may lose track of the markers. For example, if a performer lays down on the floor on his/her stomach (thereby covering a number of markers), moves around on the floor and then stands back up, the motion tracking unit 150 may not be capable of re-identifying all of the markers.
Another problem which exists with current marker-based motion capture systems is that resolution of the image capture is limited to the precision of the pattern of markers. In addition, the time required to apply the markers on to a performer is long and tedious, as the application of the markers must be precise and when a large number of markers are used, for example on a face, in practice, the markers are very small (e.g. on the order of 1-2 mm in diameter). FIGS. 2a and 2b illustrate the tediousness of the process of applying markers to a performer. The positions 202 for the application of the markers 206 must first be created with a makeup pencil 204 or other fine tip marker. Once the pattern has been created, the markers 206 are applied. Because the markers 206 are only 1-2 mm in diameter, the markers 206 must be applied to the positions 202 using tweezers (not shown) and an adhesive 208.
Another problem with current marker-based motion systems is that application of the markers must be kept away from certain areas of the performer, such as the eyes 210 and the lips 212 of a performer, because the markers may impede the free motion of these areas. In addition, secretions (e.g., tears, saliva) and extreme deformations of the skin (e.g., pursing the lips 212) may cause the adhesive 208 to be ineffective in bonding the markers 206 on certain places of the skin. Additionally, during performances with current motion capture systems, markers may fall off or be smudged such that they change position on the performer, thus requiring a halt in the performance capture session (and a waste of crew and equipment resources) to tediously reapply the markers and often recalibrate the system.
Another current approach to accomplishing motion capture is to optically project a pattern or sequence of patterns (typically a grid of lines or other patterns) onto the performer. One or more cameras is then used to capture the resulting deformation of the patterns due to the contours of the performer, and then through subsequent processing a point cloud representative of the surface of the performer is calculated. Eyetronics-3d of Redondo Beach, Calif. is one company that utilizes such an approach for motion capture.
Although projected-pattern motion capture is quite useful for high-resolution surface capture, it suffers from a number of significant limitations in a motion capture production environment. For one, the projected pattern typically is limited to a fairly small area. If the performer moves out of the area of the projection, no capture is possible. Also, the projection is only in focus within a given depth of field, so if the performer moves too close or too far from the projected pattern, the pattern will be blurry and resolution will be lost. Further, if an object obstructs the projection (e.g. if the performer raises an arm and obstructs the projection from reaching the performer's face), then the obstruction region cannot be captured. And finally, as the captured surface deforms through successive frames (e.g. if the performer smiles and the cheek compresses), the motion capture system is not able to track points on the captured surface to see where they moved from frame to frame. It is only able to capture what the new geometry of the surface is after the deformation. Markers can be placed on the surface and can be tracked as the surface deforms, but the tracking will be of no higher resolution than that of the markers. For example, such a system is described in the paper “Spacetime Faces: High Resolution Capture for Modeling and Animation”, by Li Zhang, et. al., of University of Washington.
As computer-generated animations becomes more realistic, cloth animation is used increasingly. Cloth simulation is quite complex because so many physical factors impact the simulation. This results in typically very long computation time for cloth simulation and many successive iterations of the simulation until the cloth achieves the look desired for the animation.
There have been a number of prior art efforts to capture cloth (and similar deformable and foldable surfaces) using motion capture techniques. For example, in the paper “Direct Pattern Tracking On Flexible Geometry” by Igor Guskow of University of Michigan, Ann Arbor. et. al, an approach is proposed where a regular grid is drawn on cloth and captured. More sophisticated approaches are described in other papers by Igor Guskow, et. al., such as “Multi-scale Features for Approximate Alignment of Point-based Surfaces”, “Extracting Animated Meshes with Adaptive Motion Estimation”, and “Non-Replicating Indexing for Out-of-Core Processing of Semi-Regular Triangular Surface Meshes”. But none of these approaches are suitable for a motion capture production environment. Issues include production inefficiencies such as complex preparation of a specific geometric pattern on the cloth and capture quality limitations depending on lighting or other environmental issues.
Accordingly, what is needed is an improved apparatus and method for tracking and capturing deformable and foldable surfaces in an efficient production environment.